Descripción del proyecto
Nanopartículas y aprendizaje automático para mejorar los aviones
Debido a una competencia cada vez más abundante y a normativas ambientales estrictas, el sector aeronáutico se está viendo forzado a explorar nuevas técnicas para disminuir los pesos, reducir el consumo de combustible y mitigar el impacto ambiental de los vuelos. Uno de los campos más prometedores es el de los paneles compuestos y rígidos con nanopartículas en zonas críticas y propensas a daños, ya que aportan muchas ventajas con respecto a los sistemas de sujeción mecánicos. Sin embargo, aún no hay ningún panel rígido con nanopartículas que esté certificado. Además, los mecanismos contra daños son complejos, y los métodos predictivos sobre su propagación y sobre la estimación del ciclo de vida no se han acabado de instaurar. El proyecto MACADAMIA, financiado con fondos europeos, propone como solución combinar nanopartículas con patrones basados en la física para detectar la evolución de los daños y los puntos fuertes en este tipo de paneles, así como usar el aprendizaje automático para perfeccionar la estimación del ciclo de vida.
Objetivo
MACADAMIA ambitiously seeks a seamless integration of machine learning concepts with physics-based models to optimise aerospace stiffened panels for damage tolerance. As an innovative strategy to delay damage, nanoparticles will be added in failure-prone hot-spots of composite stiffened panels to serve as damage arrest features. The efficacy of machine learning when used in conjunction with advanced computational methods for data classification and prediction will be smartly leveraged to classify and predict damage mechanisms in aircraft structures, the understanding of which is critical to their safe implementation.
In aircraft, stiffened composite panels are popular alternatives to structures with mechanical fasteners because they retain strength while reducing weight and part count; but cost and weight savings cannot be fully realized until stiffened panels are certified without fasteners in primary load-bearing structures. It is estimated that a one-pound weight reduction on each aircraft in a commercial fleet would result in fuel savings of 14000 gallons/year, which also mitigates the environmental impact of flight. To strengthen the competitiveness of European aerospace technologies in compliance with evolving environmental regulations, it is vital to work towards accelerated certification of fastener-free composite panels. Major challenges to this goal are: i) damage mechanisms in stiffened panels are complex and coupled, making the evaluation of strength and durability difficult; ii) predictive models for life-cycle estimation have large uncertainty. MACADAMIA envisions an approach with carefully designed experiments for nanoparticle inclusion along with physics-based models to investigate strength and damage evolution in stiffened panels, and machine learning to further optimise them for longer useful life. Multidisciplinary concepts of structural mechanics, computational physics, nanotechnology and machine learning will be used to accomplish research plan.
Ámbito científico
- engineering and technologymaterials engineeringcomposites
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- engineering and technologynanotechnologynano-materials
- engineering and technologyenvironmental engineeringenergy and fuels
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
2628 CN Delft
Países Bajos